Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization

A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training...

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Bibliographic Details
Published in:Expert systems with applications Vol. 245; p. 123088
Main Authors: Tian, Zhirui, Gai, Mei
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.07.2024
Subjects:
Data preprocessing strategy
Football team training algorithm
Neural network
Unconstrained weighting method
Wind speed prediction
Data preprocessing strategy
Football team training algorithm
Wind speed prediction
Neural network
Unconstrained weighting method
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:A more efficient optimization algorithm has always been the pursuit of researchers, but the performance of the current optimization algorithm in some complex test functions is not always satisfactory. In order to solve this problem, a new meta-heuristic optimization algorithm—Football Team Training Algorithm (FTTA) is proposed according to the training method of the football team, which simulates the three stages of the training session: Collective Training, Group Training and Individual Extra Training. By the test on two groups of test functions, CEC2005 and CEC2020, the proposed optimization algorithm (FTTA) achieves the best results, which far exceeds the traditional Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA) algorithms and so on. In the engineering application, a new hybrid wind speed prediction system is proposed based on FTTA. The FTTA is used to optimize variational mode decomposition (VMD) to improve the effect of data denoising. At the same time, based on unconstrained weighting algorithm, FTTA and combination prediction model build a new hybrid prediction strategy. Through the experiments on four groups of wind speed data in Dalian, the accuracy, stability, advancement, and CPU running speed of the system are verified. It is obvious that the practical application ability of the system is much better than previous methods, which can effectively improve the utilization efficiency of renewable energy.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.123088